“Point cloud” refers to the form of data obtained through three-dimensional laser scanners. Nowadays, three-dimensional laser scanners are also referred to as “LiDARs,” which rapidly acquire a large number of points on the surface of a scanned object mainly using a sensed reflected laser beam. Because each of these points contains a three-dimensional coordinate so that the LiDAR can establish a three-dimensional point cloud about the scanned object to describe the surface shape of the scanned object.
Therefore, in recent years, LiDAR has been commonly used in self-driving systems or road-sensing systems for the purpose of obstacle avoidance or tracking vehicles. However, when the scanned object is shaded or dead end of vision of LiDAR, the prior art cannot establish a three-dimensional point cloud about the scanned object and thus loses the above functions. Therefore, there is a need in the art for a way that can be used to reconstruct and predict three-dimensional point clouds.